An intelligent method to estimate the inertia matrix of a robot arm for active force control using on-line neural network training scheme
This paper presents a new intelligent controller algorithm comprising an on-line multi-layer artificial neural network (ANN) training scheme to estimate the inertia matrix of the robot arm to enhance the performance of the active force control (AFC) scheme. The robot under study is a planar two-link...
Saved in:
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Faculty of Mechanical Engineering
1999
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/8322/1/ShamsulBahriHussein1999_AnIntelligentMethodToEstimateTheInertia.PDF http://eprints.utm.my/id/eprint/8322/ http://portal.psz.utm.my/psz/index.php?option=com_content&task=view&id=128&Itemid=305&PHPSESSID=81b664e998055f65b4ccff8f61bf7cb2 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.utm.8322 |
---|---|
record_format |
eprints |
spelling |
my.utm.83222010-06-02T01:54:20Z http://eprints.utm.my/id/eprint/8322/ An intelligent method to estimate the inertia matrix of a robot arm for active force control using on-line neural network training scheme Hussein, Shamsul Bahri Jamaluddin, Hishamuddin Mailah, Musa TJ Mechanical engineering and machinery This paper presents a new intelligent controller algorithm comprising an on-line multi-layer artificial neural network (ANN) training scheme to estimate the inertia matrix of the robot arm to enhance the performance of the active force control (AFC) scheme. The robot under study is a planar two-link rigid robot which is subjected to a non-linear disturbance torques acting at the robot joints. The algorithm has two stages, namely the ANN training stage and the implementation stage. During the training stage, the proposed ANN scheme trains the ANN parameters (weights and biases) for a period of time by utilising the back-propagation (BP) learning method. After a sufficient training period, the training session is switched off, and the ANN is reay to be used in the implementation stage of the intelligent AFC-ANN controller scheme. The results of the training and implementation stages are shown and discussed. It is shown that the proposed controller scheme is very effective and robust. The simulation is accomplished using MATLAB(R) software. Faculty of Mechanical Engineering 1999-12 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/8322/1/ShamsulBahriHussein1999_AnIntelligentMethodToEstimateTheInertia.PDF Hussein, Shamsul Bahri and Jamaluddin, Hishamuddin and Mailah, Musa (1999) An intelligent method to estimate the inertia matrix of a robot arm for active force control using on-line neural network training scheme. Jurnal Mekanikal (8). ISSN 0127-3396 http://portal.psz.utm.my/psz/index.php?option=com_content&task=view&id=128&Itemid=305&PHPSESSID=81b664e998055f65b4ccff8f61bf7cb2 |
institution |
Universiti Teknologi Malaysia |
building |
UTM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Teknologi Malaysia |
content_source |
UTM Institutional Repository |
url_provider |
http://eprints.utm.my/ |
language |
English |
topic |
TJ Mechanical engineering and machinery |
spellingShingle |
TJ Mechanical engineering and machinery Hussein, Shamsul Bahri Jamaluddin, Hishamuddin Mailah, Musa An intelligent method to estimate the inertia matrix of a robot arm for active force control using on-line neural network training scheme |
description |
This paper presents a new intelligent controller algorithm comprising an on-line multi-layer artificial neural network (ANN) training scheme to estimate the inertia matrix of the robot arm to enhance the performance of the active force control (AFC) scheme. The robot under study is a planar two-link rigid robot which is subjected to a non-linear disturbance torques acting at the robot joints. The algorithm has two stages, namely the ANN training stage and the implementation stage. During the training stage, the proposed ANN scheme trains the ANN parameters (weights and biases) for a period of time by utilising the back-propagation (BP) learning method. After a sufficient training period, the training session is switched off, and the ANN is reay to be used in the implementation stage of the intelligent AFC-ANN controller scheme. The results of the training and implementation stages are shown and discussed. It is shown that the proposed controller scheme is very effective and robust. The simulation is accomplished using MATLAB(R) software. |
format |
Article |
author |
Hussein, Shamsul Bahri Jamaluddin, Hishamuddin Mailah, Musa |
author_facet |
Hussein, Shamsul Bahri Jamaluddin, Hishamuddin Mailah, Musa |
author_sort |
Hussein, Shamsul Bahri |
title |
An intelligent method to estimate the inertia matrix of a robot arm for active force control using on-line neural network training scheme |
title_short |
An intelligent method to estimate the inertia matrix of a robot arm for active force control using on-line neural network training scheme |
title_full |
An intelligent method to estimate the inertia matrix of a robot arm for active force control using on-line neural network training scheme |
title_fullStr |
An intelligent method to estimate the inertia matrix of a robot arm for active force control using on-line neural network training scheme |
title_full_unstemmed |
An intelligent method to estimate the inertia matrix of a robot arm for active force control using on-line neural network training scheme |
title_sort |
intelligent method to estimate the inertia matrix of a robot arm for active force control using on-line neural network training scheme |
publisher |
Faculty of Mechanical Engineering |
publishDate |
1999 |
url |
http://eprints.utm.my/id/eprint/8322/1/ShamsulBahriHussein1999_AnIntelligentMethodToEstimateTheInertia.PDF http://eprints.utm.my/id/eprint/8322/ http://portal.psz.utm.my/psz/index.php?option=com_content&task=view&id=128&Itemid=305&PHPSESSID=81b664e998055f65b4ccff8f61bf7cb2 |
_version_ |
1643644969225814016 |
score |
13.159267 |